Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations891
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.7 KiB
Average record size in memory113.4 B

Variable types

Numeric6
Categorical7
Text2

Alerts

Age is highly overall correlated with AgeGroupHigh correlation
AgeGroup is highly overall correlated with AgeHigh correlation
FamilySize is highly overall correlated with Fare and 3 other fieldsHigh correlation
Fare is highly overall correlated with FamilySizeHigh correlation
IsAlone is highly overall correlated with FamilySize and 2 other fieldsHigh correlation
Parch is highly overall correlated with FamilySize and 1 other fieldsHigh correlation
Sex is highly overall correlated with Survived and 1 other fieldsHigh correlation
SibSp is highly overall correlated with FamilySize and 1 other fieldsHigh correlation
Survived is highly overall correlated with Sex and 1 other fieldsHigh correlation
Title is highly overall correlated with Sex and 1 other fieldsHigh correlation
Title is highly imbalanced (57.3%) Imbalance
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
Name has unique values Unique
SibSp has 608 (68.2%) zeros Zeros
Parch has 678 (76.1%) zeros Zeros
Fare has 15 (1.7%) zeros Zeros

Reproduction

Analysis started2025-09-12 06:05:06.360973
Analysis finished2025-09-12 06:05:10.114129
Duration3.75 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-12T11:35:10.172132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2025-09-12T11:35:10.278630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
549 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Length

2025-09-12T11:35:10.391890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-12T11:35:10.442042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring characters

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Pclass
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2025-09-12T11:35:10.499113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-12T11:35:10.575531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Text

Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2025-09-12T11:35:10.759319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
james 24
 
0.7%
george 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%
2025-09-12T11:35:10.997602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Length

2025-09-12T11:35:11.067613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-12T11:35:11.110602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Age
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.361582
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-12T11:35:11.164615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile6
Q122
median28
Q335
95-th percentile54
Maximum80
Range79.58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.019697
Coefficient of variation (CV)0.44342625
Kurtosis0.99387102
Mean29.361582
Median Absolute Deviation (MAD)6
Skewness0.51024466
Sum26161.17
Variance169.5125
MonotonicityNot monotonic
2025-09-12T11:35:11.242604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 202
22.7%
24 30
 
3.4%
22 27
 
3.0%
18 26
 
2.9%
30 25
 
2.8%
19 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
36 22
 
2.5%
29 20
 
2.2%
Other values (78) 467
52.4%
ValueCountFrequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
ValueCountFrequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 4
0.4%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-12T11:35:11.303427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2025-09-12T11:35:11.356416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-12T11:35:11.410094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2025-09-12T11:35:11.466095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2025-09-12T11:35:11.668102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
ston/o 12
 
1.1%
2 12
 
1.1%
w./c 9
 
0.8%
sc/paris 9
 
0.8%
soton/o.q 8
 
0.7%
soton/oq 7
 
0.6%
Other values (709) 955
84.5%
2025-09-12T11:35:11.946683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-12T11:35:12.048607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2025-09-12T11:35:12.132710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
26.55 15
 
1.7%
Other values (238) 615
69.0%
ValueCountFrequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
S
646 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Length

2025-09-12T11:35:12.210977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-12T11:35:12.249993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s 646
72.5%
c 168
 
18.9%
q 77
 
8.6%

Most occurring characters

ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

FamilySize
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9046016
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-12T11:35:12.292059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6134585
Coefficient of variation (CV)0.84713704
Kurtosis9.159666
Mean1.9046016
Median Absolute Deviation (MAD)0
Skewness2.7274415
Sum1697
Variance2.6032485
MonotonicityNot monotonic
2025-09-12T11:35:12.341019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
6 22
 
2.5%
5 15
 
1.7%
7 12
 
1.3%
11 7
 
0.8%
8 6
 
0.7%
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
5 15
 
1.7%
6 22
 
2.5%
7 12
 
1.3%
8 6
 
0.7%
11 7
 
0.8%
ValueCountFrequency (%)
11 7
 
0.8%
8 6
 
0.7%
7 12
 
1.3%
6 22
 
2.5%
5 15
 
1.7%
4 29
 
3.3%
3 102
 
11.4%
2 161
 
18.1%
1 537
60.3%

IsAlone
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1
537 
0
354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Length

2025-09-12T11:35:12.397078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-12T11:35:12.432062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Most occurring characters

ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 537
60.3%
0 354
39.7%

AgeGroup
Categorical

High correlation 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Adult
535 
Middle-aged
195 
Teen
70 
Child
69 
Senior
 
22

Length

Max length11
Median length5
Mean length6.2592593
Min length4

Characters and Unicode

Total characters5577
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdult
2nd rowMiddle-aged
3rd rowAdult
4th rowAdult
5th rowAdult

Common Values

ValueCountFrequency (%)
Adult 535
60.0%
Middle-aged 195
 
21.9%
Teen 70
 
7.9%
Child 69
 
7.7%
Senior 22
 
2.5%

Length

2025-09-12T11:35:12.663988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-12T11:35:12.712990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
adult 535
60.0%
middle-aged 195
 
21.9%
teen 70
 
7.9%
child 69
 
7.7%
senior 22
 
2.5%

Most occurring characters

ValueCountFrequency (%)
d 1189
21.3%
l 799
14.3%
e 552
9.9%
A 535
9.6%
t 535
9.6%
u 535
9.6%
i 286
 
5.1%
M 195
 
3.5%
- 195
 
3.5%
a 195
 
3.5%
Other values (8) 561
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1189
21.3%
l 799
14.3%
e 552
9.9%
A 535
9.6%
t 535
9.6%
u 535
9.6%
i 286
 
5.1%
M 195
 
3.5%
- 195
 
3.5%
a 195
 
3.5%
Other values (8) 561
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1189
21.3%
l 799
14.3%
e 552
9.9%
A 535
9.6%
t 535
9.6%
u 535
9.6%
i 286
 
5.1%
M 195
 
3.5%
- 195
 
3.5%
a 195
 
3.5%
Other values (8) 561
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1189
21.3%
l 799
14.3%
e 552
9.9%
A 535
9.6%
t 535
9.6%
u 535
9.6%
i 286
 
5.1%
M 195
 
3.5%
- 195
 
3.5%
a 195
 
3.5%
Other values (8) 561
10.1%

Title
Categorical

High correlation  Imbalance 

Distinct22
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Mr
502 
Miss
182 
Mrs
122 
Master
 
40
Rare
 
20
Other values (17)
 
25

Length

Max length11
Median length2
Mean length2.8597082
Min length1

Characters and Unicode

Total characters2548
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.5%

Sample

1st rowMr
2nd rowMrs
3rd rowMiss
4th rowMrs
5th rowMr

Common Values

ValueCountFrequency (%)
Mr 502
56.3%
Miss 182
 
20.4%
Mrs 122
 
13.7%
Master 40
 
4.5%
Rare 20
 
2.2%
y 4
 
0.4%
Planke 3
 
0.3%
Impe 3
 
0.3%
Gordon 2
 
0.2%
Billiard 1
 
0.1%
Other values (12) 12
 
1.3%

Length

2025-09-12T11:35:12.773990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr 502
56.3%
miss 182
 
20.4%
mrs 122
 
13.7%
master 40
 
4.5%
rare 20
 
2.2%
y 4
 
0.4%
planke 3
 
0.3%
impe 3
 
0.3%
gordon 2
 
0.2%
billiard 1
 
0.1%
Other values (12) 12
 
1.3%

Most occurring characters

ValueCountFrequency (%)
M 849
33.3%
r 693
27.2%
s 531
20.8%
i 184
 
7.2%
e 86
 
3.4%
a 70
 
2.7%
t 42
 
1.6%
R 20
 
0.8%
l 12
 
0.5%
n 7
 
0.3%
Other values (18) 54
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 849
33.3%
r 693
27.2%
s 531
20.8%
i 184
 
7.2%
e 86
 
3.4%
a 70
 
2.7%
t 42
 
1.6%
R 20
 
0.8%
l 12
 
0.5%
n 7
 
0.3%
Other values (18) 54
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 849
33.3%
r 693
27.2%
s 531
20.8%
i 184
 
7.2%
e 86
 
3.4%
a 70
 
2.7%
t 42
 
1.6%
R 20
 
0.8%
l 12
 
0.5%
n 7
 
0.3%
Other values (18) 54
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 849
33.3%
r 693
27.2%
s 531
20.8%
i 184
 
7.2%
e 86
 
3.4%
a 70
 
2.7%
t 42
 
1.6%
R 20
 
0.8%
l 12
 
0.5%
n 7
 
0.3%
Other values (18) 54
 
2.1%

Interactions

2025-09-12T11:35:09.471357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.000506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.488949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.954053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.406878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.053261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.542596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.120546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.567943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.028226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.484591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.119434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.604540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.208293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.643936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.099432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.577125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.190609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.678591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.279251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.715239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.185618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.659891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.275078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.740530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.350304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.788166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.262682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.907520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.342642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.805761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.423658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:07.874959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.333050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:08.978731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-12T11:35:09.407954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-12T11:35:12.822989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAgeGroupEmbarkedFamilySizeFareIsAloneParchPassengerIdPclassSexSibSpSurvivedTitle
Age1.0000.8190.151-0.1830.1260.348-0.2170.0350.2650.106-0.1450.1580.238
AgeGroup0.8191.0000.0770.2630.0890.3750.2780.0000.2410.1270.2570.1180.394
Embarked0.1510.0771.0000.0830.1950.1100.0520.0000.2580.1110.0920.1640.143
FamilySize-0.1830.2630.0831.0000.5290.6420.801-0.0500.1370.2050.8490.2150.157
Fare0.1260.0890.1950.5291.0000.3040.410-0.0140.4790.1890.4470.2830.000
IsAlone0.3480.3750.1100.6420.3041.0000.6860.0000.1270.3000.8370.1980.499
Parch-0.2170.2780.0520.8010.4100.6861.0000.0010.0220.2470.4500.1570.185
PassengerId0.0350.0000.000-0.050-0.0140.0000.0011.0000.0320.066-0.0610.1040.043
Pclass0.2650.2410.2580.1370.4790.1270.0220.0321.0000.1300.1480.3370.183
Sex0.1060.1270.1110.2050.1890.3000.2470.0660.1301.0000.2060.5400.979
SibSp-0.1450.2570.0920.8490.4470.8370.450-0.0610.1480.2061.0000.1870.223
Survived0.1580.1180.1640.2150.2830.1980.1570.1040.3370.5400.1871.0000.569
Title0.2380.3940.1430.1570.0000.4990.1850.0430.1830.9790.2230.5691.000

Missing values

2025-09-12T11:35:09.914153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-12T11:35:10.024611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedFamilySizeIsAloneAgeGroupTitle
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S20AdultMr
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C20Middle-agedMrs
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250S11AdultMiss
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S20AdultMrs
4503Allen, Mr. William Henrymale35.0003734508.0500S11AdultMr
5603Moran, Mr. Jamesmale28.0003308778.4583Q11AdultMr
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625S11Middle-agedMr
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750S50ChildMaster
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333S30AdultMrs
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708C20TeenMrs
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedFamilySizeIsAloneAgeGroupTitle
88188203Markun, Mr. Johannmale33.0003492577.8958S11AdultMr
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167S11AdultMiss
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000S11AdultMr
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500S11AdultMr
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250Q60Middle-agedMrs
88688702Montvila, Rev. Juozasmale27.00021153613.0000S11AdultRare
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000S11AdultMiss
88888903Johnston, Miss. Catherine Helen "Carrie"female28.012W./C. 660723.4500S40AdultMiss
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C11AdultMr
89089103Dooley, Mr. Patrickmale32.0003703767.7500Q11AdultMr